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The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R2 values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project. [ABSTRACT FROM AUTHOR]

Viruses with rapid replication and easy mutation can become resistant to antiviral drug treatment. With novel viral infections emerging, such as the recent COVID-19 pandemic, novel antiviral therapies are urgently needed. Antiviral proteins, such as interferon, have been used for treating chronic hepatitis C infections for decades. Natural-origin antimicrobial peptides, such as defensins, have also been identified as possessing antiviral activities, including direct antiviral effects and the ability to induce indirect immune responses to viruses. To promote the development of antiviral drugs, we constructed a data repository of antiviral peptides and proteins (DRAVP). The database provides general information, antiviral activity, structure information, physicochemical information, and literature information for peptides and proteins. Because most of the proteins and peptides lack experimentally determined structures, AlphaFold was used to predict each antiviral peptide's structure. A free website for users (http://dravp.cpu-bioinfor.org/ , accessed on 30 August 2022) was constructed to facilitate data retrieval and sequence analysis. Additionally, all the data can be accessed from the web interface. The DRAVP database aims to be a useful resource for developing antiviral drugs. [ABSTRACT FROM AUTHOR]

The determination of core genes in viral and bacterial genomes is crucial for a better understanding of their relatedness and for their classification. CoreGenes5.0 is an updated user-friendly web-based software tool for the identification of core genes in and data mining of viral and bacterial genomes. This tool has been useful in the resolution of several issues arising in the taxonomic analysis of bacteriophages and has incorporated many suggestions from researchers in that community. The webserver displays result in a format that is easy to understand and allows for automated batch processing, without the need for any user-installed bioinformatics software. CoreGenes5.0 uses group protein clustering of genomes with one of three algorithm options to output a table of core genes from the input genomes. Previously annotated "unknown genes" may be identified with homologues in the output. The updated version of CoreGenes is able to handle more genomes, is faster, and is more robust, providing easier analysis of custom or proprietary datasets. CoreGenes5.0 is accessible at coregenes.org, migrating from a previous site.

Evidence that Hepatitis D virus (HDV) genotype is involved in HDV infection pathogenesis is increasing. Indeed, HDV genotypes have been shown to be linked to different outcomes in terms of liver fibrosis and treatment response. Herein, we show that the promising HDVdb genotyping tool available online can lead to wrong genotyping results. The current HDVdb algorithm should be carefully considered as a "beta-version" and warrants algorithm core corrections, as soon as possible, for an optimal and beneficial use. [ABSTRACT FROM AUTHOR]

The merger of SCADA (supervisory control and data acquisition) and IoTs (internet of things) technologies allows end-users to monitor and control industrial components remotely. However, this transformation opens up a new set of attack vectors and unpredicted vulnerabilities in SCADA/IoT field devices. Proper identification, assessment, and verification of each SCADA/IoT component through advanced scanning and penetration testing tools in the early stage is a crucial step in risk assessment. The Omega2, a small Linux server from Onion™, is used to develop various SCADA/IoT systems and is a key component of nano power grid systems. In this paper, we report product level vulnerabilities of Onion Omega2 that we have uncovered using advanced vulnerability scanning tools. Through this research, we would like to assist vendors, asset owners, network administrators, and security professionals by creating an awareness of the vulnerabilities of Onion Omega2 and by suggesting effective mitigations and security best practices. [ABSTRACT FROM AUTHOR]

Hepatitis D virus (HDV) causes the most severe form of viral hepatitis, which may rapidly progress to liver cirrhosis and hepatocellular carcinoma (HCC). It has been estimated that 15–20 million people worldwide are suffering from the chronic HDV infection. Currently, no effective therapies are available to treat acute or chronic HDV infection. The remarkable sequence variability of the HDV genome, particularly within the hypervariable region has resulted in the provisional classification of eight major genotypes and various subtypes. We have developed a specialized database, HDVdb, which contains a collection of partial and complete HDV genomic sequences obtained from the GenBank and from our own patient cohort. HDVdb enables the researchers to investigate the genetic variability of all available HDV sequences, correlation of genotypes to epidemiology and pathogenesis. Additionally, it will contribute in understanding the drug resistant mutations and develop effective vaccines against HDV infection. The database can be accessed through a web interface that allows for static and dynamic queries and offers integrated generic and specialized sequence analysis tools, such as annotation, genotyping, primer prediction, and phylogenetic analyses. [ABSTRACT FROM AUTHOR]

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method. [ABSTRACT FROM AUTHOR]

Evidence that Hepatitis D virus (HDV) genotype is involved in HDV infection pathogenesis is increasing. Indeed, HDV genotypes have been shown to be linked to different outcomes in terms of liver fibrosis and treatment response. Herein, we show that the promising HDVdb genotyping tool available online can lead to wrong genotyping results. The current HDVdb algorithm should be carefully considered as a "beta-version" and warrants algorithm core corrections, as soon as possible, for an optimal and beneficial use.